Rating forest stand susceptibility to southern pine beetle in East Texas

Forest Ecology and Management, 2 (1980) 269--283 Elsevier Scientific Publishing Company, A m s t e r d a m - - Printed in The Netherlands

269

RATING FOREST STAND SUSCEPTIBILITY TO SOUTHERN PINE BEETLE IN EAST TEXAS 1

RAY R. HICKS, Jr.,*, JAMES E. HOWARD**, KENNETH G. WATTERSTON** and JACK E. COSTER***

* Division o f Forestry, West Virginia University, Morgantown, W. Va. 26506 (U.S.A.) ** School of Forestry, Stephen F. Austin State University, Nacogdoches, Texas 75962

(U.S.A.) ***Southern Pine Beetle Program, 2500 Shreveport Highway, Pineville, La, 71360 (U.S.A.) ' Funds for this research were made available to Stephen F. Austin State University by the U.S.D.A., Expanded Southern Pine Beetle Research and Applications Program. (Accepted 21 January 1980)

ABSTRACT Hicks, R.R., Jr., Howard, J.E., Watterston, K.G. and Coster, J.E., 1980. Rating forest stand susceptibility to southern pine beetle in East Texas. Forest Ecol. Manage., 2: 269--283. A stand rating system was developed from site and stand data collected from 900 beetle-infested and non-infested pine stands in eastern Texas. The model predicts the probability of southern pine beetle attack over a 3-year period and requires data for pine basal area/ha, average tree height, and a categorical evaluation of landform. All variables can readily be determined in the field. Predicted probabilities of attack are valid given certain geographical constraints and assuming epidemic beetle population levels.

INTRODUCTION

The southern pine beetle (Dendroctonous frontalis Zimm.) is the single most destructive forest pest in East Texas. From 1975 to 1977, the Texas Forest Service (1976, 1978) reported that 73,658 million cubic feet (approx. 2,000 million m 3 ) of timber was damaged by the insect. The southern pine beetle (SPB) population in East Texas has experienced extreme fluctuations since 1900 with serious epidemics occurring in 1920, 1949--1955, 1965--1968 and 1972--1977 (Weitzmann, 1975). Peak activity during epidemics has generally increased with each succeeding occurrence and a peak of about 11,000 infestation spots was reported by the Texas Forest Service (1978) during the most recent outbreak. Beetle population extremes are partly a result of weather variability (King, 1972; Kalkstein,

0 3 7 8 - - 1 1 2 7 / 8 0 / 0 0 0 0 - - 0 0 0 0 / $ 02.25, © 1980 - - Elsevier Scientific Publishing Company

270 1976; KroU and Reeves, 1978) but the underlying cause of the successive increases may be related to changing stand conditions (Hedden, 1978). Promotion of stand resistance through silvicultural measures appears to be a viable method for reducing the impact of the southern pine beetle (Coster, 1977). Susceptibility of stands to SPB infestations may result from site factors (Lorio, 1968; Belanger et al., 1976, 1979; Ku et al., 1976) or from overstocking (Lorio and Bennett, 1974; Leuschner et al., 1976) and resulting loss in vigor (Coulson et al., 1974; Hicks et al., 1978a). Diseases and stand disturbances may also trigger infestations (Hodges and Pickard, 1971; Bradford and Skelly, 1976). Silvicultural controls have been proposed for several bark beetle species (Miller and Keen, 1960; Bennett, 1968, 1971; Sartwell, 1971; Safranyik et al., 1974; Sartwell and Stevens, 1975; Cole and Cahill, 1976;~ bllin, 1976; Sartwell and Dolph, 1976). A stand hazard-rating system would permit the forest manager to identify the most susceptible stands and rank them in order of priority of needed silvicultural treatment. Hazard rating would also facilitate surveillance and planning of control activities. Stand hazard-rating systems are available for several bark beetles. Some incorporate site and stand factors (Miller and Keen, 1960; Sader and Miller, 1975; Lorio, 1978). Tree competition and stocking measures are often important in hazard-rating systems (Hamilton and Edwards, 1976; Schmid and Frye, 1976; Schenk et al., !977; Stage and Long, 1977). Climatic factors are incorporated into some models (Leuschner et al., 1977) and anatomical characteristics of the tree are used in others (Berryman, 1976). This paper presents a simple SPB hazard-rating system for East Texas. It is based upon site and stand variables that can easily be evaluated in the field. FIELD SAMPLINGMETHODS The data for our model were collected from Nacogdoches, Angelina and San Augustine Counties in East Texas (Fig.l). These counties have a total land area of 0.63 million ha (1.56 million acres) of which approximately 65% is forested (Earles, 1976). The area is geographically centered in the East Texas pineywoods and is representative of much of the 2.75 million ha (6.8 million acres) of pine and pine-hardwood stands in the region. Individual beetle infestations (spots) were located with the aid of aerial surveillance reports from the Texas Forest Service. These surveys reported only spots with 10 or more red and fading trees. During 1975 and 1976 the spots to be sampled were chosen randomly from the surveillance reports. However, it became apparent that beetle activity was not uniform over the area and the 1977 plots were collected so as to ensure a more representative sample. For this purpose, the number of beetle spots occurring from 1974 to 1976 was tallied for each 8.04 × 8.04 km (5 × 5 mile) grid square in the study area. In 1977 plots were located to assure that the number of plots was proportional to the intensity of infestation in each grid square. A total of 484 infested plots was used in developing the hazard-rating system.

271

IAL

'UTH AST

Fig.1. Site-stand study area in East Texas showing subdivision of East Texas counties into southern and central units for stand rating purposes.

Our non-infested plots are termed baseline plots, the purpose o f which is to provide an estimate o f the typical forest conditions within the area. In 1975 and 1976 t w o baseline plots were randomly located in each grid square. A post-stratification process was implemented in 1977 so that additional baseline plots were located in grid squares proportional to the percent of host-type forest land within the squares. A total of 416 baseline plots was used for this study. Using sample allocation analysis, it was determined that this number adequately characterized the s t u d y area (Bozeman, 1977). A 10-factor basal area prism was used to establish variable-radius plots. For beetle-infested plots, the center was located near the origin of the infestation. Centers for baseline plots were established from random coordinates with the stipulation that the plot must fall within a forested area. Data were taken on all 'in'trees. Stand and tree data included species, basal areas for pine and hardwood, averages for age, height and diameter at breast height (dbh), radial growth for the most recent 5 years and the previous 5 years, bark thickness at the ridges and fissures, and crown position class. Evidence of disturbances such as lightning, logging, ice breakage and disease was noted.

272 Sites were classified according to water regimes, landform and soil texture classes (U.S.D.A., 1972). Site index was determined and a soil sample was collected and returned to the laboratory for analyses of chemical and physical properties of the 'A' and 'B' horizons. Number of trees killed and area of the infestation were estimated for beetle-infested plots. Only plots in forest types loblolly pine, shortleaf pine (i> 50% pine), loblolly pine--hardwoods, and shortleaf pine--oak (25--49% pine) (U.S.D.A., 1972) were used in developing the model since these are the host-type stands. DEVELOPING THE STAND RATING SYSTEM

Selecting variables for inclusion in the model The initial step was to determine the variables most strongly associated with SPB infestations. For this purpose, the data were analysed b y stepwise discriminant analysis using F levels for inclusion and deletion of 1.000 and a tolerance level of 0.001 (Nie et al., 1975). From the significant variables describing the baseline and infested plots, a mathematical equation was developed w h e r e b y an u n k n o w n plot could be classified depending on the value of its discriminant score. The stepwise solution permitted the ranking of variables in order o f their usefulness in differentiating b e t w e e n baseline and infested plots. Discriminant analyses were performed on the lobloUy and shortleaf pine forest types (including their respective pine-hardwood mixtures) separately and in combination. The ranking of variables was similar in each case (Table I) and the averages for variables did n o t differ appreciably between forest types (Table II). Thus loblolly and shortleaf pine forest types were combined for purposes of stand rating. The variables bark thickness in fissures, pine basal area, average tree height and landform were important in developing the discriminant function. Treating the plots as unknowns, 79% were correctly classified as to infestation status using this analysis. Absolute values for the standardized discriminant coefficients calculated for each variable are indicative of the relative importance of the variables in classification. These values were used as weighting criteria for variables in the stand-rating model. The coefficients for bark thickness and pine basal area are a b o u t 1.3 times as large as for average height in the combined forest t y p e analysis and the values decrease sharply thereafter (Table I). The degree of correlation among variables was also a consideration in deciding which variables to include in the stand-rating model. Since variables that are highly correlated with one another contain much of the same mathematical information, it would n o t improve the model to include t w o or more highly correlated variables. A correlation matrix among all variables used in the discriminant model revealed that average height was slightly correlated with pine basal area (r ~ 0.1) b u t otherwise, significant correlations were n o t found.

Bark thickness Pine basal area Average height Topsoil depth Landform Site index Surface soil texture Average dbh Radial growth I Radial growth II 3 Subsoil texture

81%

Std.Discrim. Coeff. -0.51976 -0.51390 -0.46932 0.28095 0.14843 0.16843 0.10774 0,15706 0.21081 -0.13295 0.07951 Bark thickness Pine basal area Average height Landform Site index Average dbh Radial growth I Topsoil depth Surface soil texture Subsoil texture Water regime Hardwood basal area

Variable

Combined

79%

Std. Discrim. Coeff. -0.51161 -0.51526 -0.40455 0.17528 0.13538 0.17002 0.12525 0.18884 0.10389 0.10514 0.08937 0.07829

1 Measured in the fissures. 2 Increment during the most recent 5 years. 3 Increment during the 5 years previous to the most recent 5 years. 4 Percent o f plots correctly classified as to infestation status using the particular discriminant function.

Classification effectiveness: 4 76%

Bark thickness I Pine basal area Radial growth I ~ Landform Average height Average dbh Hardwood basal area Site index Surface soil texture

Variable

Variable

Std. Discrim. Coeff. -0.52809 -0.47118 0.23018 0.21063 -0.36305 0.20341 0.11351 0.13272 0.09219

Shortleaf pine

Loblolly pine

Forest type

Variables entered into disc~iminant equations in order of inclusion for the various forest types separately and combined

TABLE I

t~

0.569 ± 2.504

27.41 ± 8.47

19.20 + 4.05

23.45 +- 3.55

27.15 ± 6.43

15.35 + 5.38

Basal area pine (sq. m / h a )

Average height (m)

Site i n d e x (m)

Average d b h (cm)

Most r e c e n t 5-year increment (mm) 18.99 ± 7.54

27.18 +- 7.32

23.28 ± 3.44

17.54 ± 4.74

21.72 + 8.54

0.381 ± 1.595

12.26 ± 4.72

26.62 + 6.10

23.05 ± 4.14

19.69 ± 4.45

25.48 ± 8.51

0.572 ± 0.290

Infested

Infested

Baseline

S h o r t l e a f pine

Loblolly pine

Fissure bark t h i c k n e s s (cm)

Variable

Forest type

14.90 + 5.94

26.14 ± 6.83

22.40 ± 3.58

17.01 ± 4.67

19.90 ± 8.24

0.350 ± 0.136

Baseline

Means and s t a n d a r d deviations f o r variables i m p o r t a n t in developing d i s c r i m i n a n t ,equations

T A B L E II

14.30 +- 5.36

26.97 +- 6.32

23.31 ± 3.76

19.36 -+ 4.19

26.77 ± 8.52

0.572 ± 0.264

Infested

Combined

17.05 ± 7.12

26.67 ± 7.08

22.87 + 3.53

17.29 ± 4.70

20.86 + 8.43

0.368 ± 1.496

Baseline

tO

275 Based solely on our statistical results, the best stand-rating model for the combined forest types would include all the variables shown in the appropriate column in Table I. Deletion of some variables was intuitively desirable due to the difficulty of sampling them in the forest and in the interest of simplifying the model. The standardized discriminant coefficients for fissure bark thickness, pine basal area and average height were a b o u t t w o times larger than the next higher coefficients. But bark thickness in fissures is extremely difficult to measure in the field. There are t w o problems; first is the rather large variance in the trait itself (Table II), and second is the potential for sampling error in measurement. Concerning the latter, bark fissures are often narrower than the flange on a standard bark gauge, thus the instrument does n o t accurately measure this thickness. This is coupled with the large rounding errors possible due to the scale used on the bark gauge. Our final model, therefore, included pine basal area, average height and landform. The latter was included to incorporate a site variable and landform was the next variable identified in the stepwise discriminant analysis. Landform categories used were swamp, flood plain, stream terrace, bay, upland fiat, lower slope, side slope, steep side slope and ridge, as defined b y the U.S.D.A. (1972). The variables used in the rating model are all readily measurable in the field (Hicks et al., 1978b). Standardized discriminant coefficients were - 0 . 7 7 6 7 8 for pine basal area, - 0 . 4 1 6 5 6 for average height and 0.36386 for landform. With this model, 71.5% of the plots were correctly classified using discriminant analysis when treated as unknowns.

Estimating the overall probability of attack Estimates for the overall attack probability were obtained from our own 3-year data, the Texas Forest Service (1976, 1978) and from Leuschner et al. (1976). These estimates varied considerably. Reasons for these differences and the limitations and specific utilities of each estimate are discussed in succeeding paragraphs. Field crews in our project estimated the average area of each spot to the nearest 0.10 ha (0.25 acres). The average area of all spots was 0.17 ha (0.43 acres) and the number of spots (greater than 10 trees in size) for the study area over the 3 years was 1,618 (Texas Forest Service, 1976, 1978). The resulting estimate for total area attacked was 283.40 ha (700.27 acres). An accurate estimate o f average spot size is difficult to obtain due to several factors. First, many spots were still active at the time of measurement and therefore had n o t reached their m a x i m u m size, and second, trees were cut from some active spots as a result o f Texas Forest Service pest control activities. According to Earles (1976) there are 481,876 ha (1,190,700 acres) of commercial forest land in the study area, of which 304,700 ha are SPB host-types (loblolly--shortleaf pine and oak--pine). The overall 3-year attack probability was therefore estimated to be 2 8 3 . 4 0 / 3 0 4 , 7 0 0 or 0.00093, based upon our data for spot size.

276

A second estimate, obtained from the data of Leuschner et al. (1976), was based u p o n all spots occurring in the Trinity District of the Davy Crockett National Forest between July 1974 and June 1975. They examined 477 spots, including those with less than 10 beetle-killed trees. The average spot size was calculated to be 0.0308 ha (0.0762 acre). Assuming the same rate of occurrence over a 3-year period, the infested area would be: 477 spots X 3 X 0.0308 ha/spot = 44.07 ha. The total area of host forest t y p e in the Trinity District is approximately 32,375 ha (80,000 acres), the 3-year overall attack probability would be estimated as 4 4 . 0 7 / 3 2 , 3 7 5 = 0.00136, or similar to that obtained using our spot size data. A third estimate of overall attack probability was obtained from data of the Texas Forest Service (1976, 1978). These reports indicate that a total of 24,040 ha (59,402 acres) were SPB-attacked during 1975--1977 over the entire East Texas area. A total of 2,742,200 ha (6,775,900 acres) are in SPB host-types in East Texas (Earles, 1976). Thus the 3-year overall attack probability based on these data would be 0.0876. The estimates of overall attack probability vary from a low of approximately 1 in 1,000 from our data and those of the Trinit'y District data, to almost 1 in 100 from Texas Forest Service information. Our data represent a lower limit because we used only spots of 10 trees or larger and because we were working in an area of moderate beetle activity. Data from Leuschner et al. (1976) are the most comprehensive in that all spots were measured regardless of size, b u t their study area is also in a moderate beetle activity zone, thus overall attack probability estimated from these data was similar to ours. The highest overall probability was estimated from Texas Forest Service data. This seems to be due to the occurrence of several large infestations in southeastern Texas (40 ha) which have considerably affected the average spot size (approximately 1.2 ha). Spots in southeastern Texas are n o t 60

• So

INFESTED

UlIII BASELINE

2O

°tl

<12.3

12.4-18.5,

18.6-24.7

b_ >24.7

HEIGHT CLASS ( m )

Fig.2. Distribution of infested and baseline samples by tree height classes.

277 TABLE III SPB spot occurrences from 1975 to 1977, estimated infested hectares, total hectares of host forest type and estimated overall attack probabilities by counties [Texas Forest Service (1976, 1978) and Earles (1976)] County

No. SPB spots 1975

Southeastern counties Hardin Liberty Montgomery Newton

Orange Polk San Jacinto Tyler Walker All Counties Central counties Anderson Angelina Cherokee Houston Nacogdoches Panola Rusk Sabine San Augustine Shelby Trinity All Counties

1976

1977

Total

Area infested' (ha)

Area host-type (ha)

Overall attack probability

732 570 266 175 149 325 281 245 173

1,425 990 1,059 417 267 894 209 626 236

765 467 506 107 137 374 51 284 62

2,922 2,027 1,831 699 533 1,593 541 1,155 471

3,441.17 2,387.15 2,156.32 823.20 651.26 1,876.04 637.12 1,360.22 554.68

110,321 57,872 172,564 135,980 21,166 197,170 94,578 145,652 107,326

0.03119 0.04125 0.01249 0.00605 0.03077 0.00951 0.00674 0.00934 0.00517

2,916

6,123

2,753

11,792

13,887.16

1,042,629

0.01332

56 269 124 212 128 29 41 110 84 127 184

206 429 418 162 298 152 153 144 133 212 254

26 97 97 22 84 55 45 55 91 196 33

288 795 640 396 510 236 239 309 308 535 471

339,17 936.25 753.71 466.36 600.61 277.93 281.46 363.90 362.72 630.06 554.68

56,779 121,734 89,236 116,553 96,157 85,958 65.238 93,486 86,808 97,614 130,880

0.00597 0.00769 0.00845 0.00400 0.00625 0.00323 0.00431 0.00389 0.00418 0.00645 0.00424

1,364

2,562

801

4,727

5,566.85

1,040,443

0.00535

A~uming an average spot size of 1.18 ha.

only larger than in our study area but are more numerous; nine southeastern counties contained 60% of all spots reported in 1976 while the remaining 40% were in 29 other counties. The area has been the site of chronic beetle epidemics in Texas, hence, the Texas Forest Service data are heavily weighted with infestations from southeastern Texas and the overall attack probability calculated from them would be more appropriate to the southeastern counties. To facilitate an estimate of overall attack probability specifically for southeastern and central eastern Texas, the Texas Forest Service data were separated according to these geographic subunits (Fig.l). Overall attack probability for the southeastern counties was 0 . 0 1 3 3 2 or about 1 in 75, while the same value for the east-central counties was 0 . 0 0 5 3 5 or about 1 in 200 (Table III). The latter value is about five times as large as the estimate obtained from Leuschner et al. (1976) and probably reflects the fact that the Texas Forest Service assumed an average spot size of 1.2 ha for all spots (in both southeastern and central eastern counties). This may be a considerable overestimate for the latter as evidenced by the average spot size of about 0.17 ha obtained from our data.

278 )-

o 0

II

INFESTED

lllIllll B A S E L I NE

SO

I-4O Z LU

30

o '~ 2O Z U I0

0-9.2

9.3-18A BASAL

18.5-27.5

>2Z5

AREA CLASS ( m2/ha )

Fig.3. Distribution of infested and baseline samples by pine basal area classes

Calculating probability of beetle attack The stand rating system was formulated using the baseline data as representing the typical forest conditions and the infested plots as representing the infested acreage in the study area. To obtain the probability of attack for a given set of stand and site conditions, the overall attack probability (A) was multiplied by coefficients for each of the three variables to obtain the probabilities due to individual variables (Pi). For this purpose, the ranges of the variables were divided into classes. Within each class, the coefficient representing the deviation of percent SPB occurrence in infested versus baseline plots (D) was calculated as the ratio of percent occurrence of infested

) . 50

i

II

INFESTED

IIIIIIII BAS E L I N E

.~.4o i

z 3o

Z ul w n

I0

FLOOD STREAM BAY UPLAND LOWER SIDE STEEPSIDE PLAIN TERR. FLAT SLOPE SLOPE SLOPE CATEGORY Fig.4. Distribution of infested and baseline samples by landform classes.

279 to baseline plots within that class (Figs 2, 3 and 4). For example D for the 9.3--18.4 m 2 basal area class is 19.61%/40.10% = 0.489. The weight (W) assigned to a particular variable was based u p o n the standardized coefficients from discriminant analysis. Weights were assigned so as to sum to 1.0, and the contributions of individual variables could be added to obtain the probability o f attack {Pa) as: 3 Probability of attack (Pa) = i=Z1Pi where: Pi=AXDX

W

and: Pi = probability due to individual variable; A = overaU probability o f attack; D = infested : baseline deviation; W = variable weight. Several qualifications of the data and the stand rating system should be kept in mind. (1) The basic data were obtained from Angelina, Nacogdoches and San Augustine Counties, Texas. The stand rating system is p r o b a b l y useful for most o f East Texas, however, since forest conditions are generally similar to the study area. (2) The probabilities were derived when beetle populations in the s t u d y area were at very high levels. (3) The probability of attack expresses the risk of loss over a 3-year period, i.e. one-third o f this value is the annual probability. (4) Probabilities of attack are expressed on a per-hectare basis. To determine the unit area probability of loss within a homogeneous tract of any size, multiply the value o f Pa b y the area of the tract. APPLYING THE RATING SYSTEM Field data collection (1) Stratify the area to be rated b y c o u n t y or georgraphic subregion so as to obtain the appropriate estimate of 'A' from Table III. (2) Stratify the subject area b y landform classes and forest type. Topographic maps and aerial photographs m a y be useful for this purpose. (3) Sample only within lobl011y pine, shortleaf pine or pine--hardwood stands containing greater than .25% pine. (4) Establish point sample plots within these stands. The intensity o f sampling will depend u p o n landowner objectives and the variability within stands. A more detailed discussion o f sample allocation is available from Avery {1967). Perhaps the older, larger stands o f pure pine should be sampled more intensively since these stands are more valuable and p r o b a b l y more susceptible to beetles than most other stands.

280 (5) Determine basal area at the sample points using a 2.3-m 2 (10 sq. ft.) basal area factor prism or angle gauge and measure the heights of the 'in' trees. These variables should then be averaged for each stand to be rated.

Determining probability of attack The estimates of overall attack probability (A) for different geographic areas vary considerably (Table III). Obviously, use of different values of A will yield different estimates of probability o f attack (Pa). It becomes a matter of judgement as to which estimate is appropriate to a given situation. The estimate of A from our data is a lower limit value, as is the Trinity District estimate, and would be appropriate for use in central and northeastern Texas counties where beetle activity is relatively low. The estimate derived from Texas Forest Service data is more appropriate to the highactivity areas in southeastern Texas. Table IV illustrates probabilities calculated for individual variables (Pi) using our estimate of A. The 3-year probabilities o f attack may be obtained by summing the appropriate Pi values for the three variables. The lowest possible value of Pa is 0 . 0 0 0 1 9 6 or 1 / 5 1 0 0 using the estimate of A from our data and stand and site conditions in the least susceptible categories. The highest possible estimate is 0 . 0 4 5 0 5 8 or about 1 / 2 2 which is obtained using the value of A estimated from the Texas Forest Service data for southeastern Texas and the most susceptible conditions. TABLE IV 3-year probabilities o f SPB a t t a c k per hectare using t h e estimate o f overall a t t a c k p r o b a b i l i t y f r o m o u r t h r e e - c o u n t y s t u d y area Variable

V a l u e class

(A) X Overall attack probability

(D) X Infested: baseline ratio

(W)

Weight

(Pi) Prob. d u e to variabla

X

Average height ( m )

~12.3 12.4--16.5 16.6--24.7 >24.7

0.00093 0.00093 0.00093 0.00093

0.24 0.76 1.59 1.25

0.268 0.266 0.268 0.268

0.000060 0.000189 0.000396 0.000311

Pine basal area (m2/ha)

0--9.2 9.3--18.4 18.5--27.5 >27.5

0.00093 0.00093 0.00093 0.00093

0.151 0.489 1.422 2.715

0.498 0.498 0.498 0.498

0.000070 0.000226 0.000658 0.001257

0.00093 0.00093 0.00098 0.00093 0.00093 0.00093 0.00093

2.846 6.857 1.023 1.416 0.739 0.462 0.306

0.234 0.234 0.234 0.234 0.234 0.234 0.234

0.000619 0.001492 0.000223 0.003061 0.000161 0.000100 0.000066

Landform (category)

Floodplain S t r e a m terrace U p l a n d fiat l.~ wer s l o p e Side s l o p e Steep side s l o p e Ridge

3 P r o b a b i l i t y o f a t t a c k ( P a ) = • Pi X acreage o f stand. 1=1 Range ffi 0 . 0 0 0 1 9 6 - - 0 . 0 0 3 1 4 5 ( 1 / 5 1 0 2 ) - - - ( 1 / 3 1 8 ) .

281

CONCLUSIONS The risk-rating model uses stand and site variables t o estimate a t t a c k probability f o r geographic subunits o f East Texas. However, estimates are valid o n ly f o r specific circumstances relative t o beetle p o p u l a t i o n levels. The actual f r e q u e n c y of a t t a c k depends on b o t h host conditions and SPB p o p u l a t i o n levels. The f o r m e r is well d o c u m e n t e d in our s t u d y b u t we sampled during a specific range o f beetle popul a t i on variability. For example, during 1976 th e Texas Forest Service located a b o u t 11,000 beetle spots over the entire East Texas area; in 1978 t he r e were o n l y 40. Obviously changes in host resistance c a n n o t a c c o u n t f or such radical differences in beetle activity, and factors such as weather and biological agents must also have a significant impact. The utility o f the stand rating models will vary with land m a n a g e m e n t objectives b u t several uses are apparent. Surveillance and direct c o n t r o l tactics can be c o n c e n t r a t e d in areas with an a bunda nce o f high-hazard stands to maximize managerial efficiency. One o f th e most appealing uses o f stand rating is in planning stand managem e n t strategies. Mature, high-hazard stands should be harvested as soon as possible. Selective thinnings could be used to r e m ove slower growing trees in overstocked stands. It would be possible to d e t e r m i n e t he n u m b e r o f such trees t h a t would have to be r e m o v e d f r o m t he stand in order to achieve a specified level o f a t t a c k probability. The limitations o f t he stand rating m e t hods must be t aken into a c c o u n t when applying th em, but with p r o p e r use, t he model can be a valuable t ool fo r decision making in East Texas forest management. REFERENCES Avery, T.E., 1967. Timber Measurements. McGraw-Hill, New York, N.Y., 290 pp. Belanger, R.P., Hatchell, G.E. and Moore, G.E., 1976. Soil and stand characteristics related to southern pine beetle infestations: a progress report for Georgia and NorthCarolina. Proc. 6th Southern Forest Soils Workshop. Charleston, S.C., pp. 99--107. Belanger, R.P., Osgood, E.A. and Hatchell, G.E., 1979. Stand, soil and site characteristics associated with southern pine beetle infestations in the southern Appalachians. U.S.D. A., Forest Serv. Res. Paper SE-198, 7 pp. Bennett, W.H., 1968. Timber management and southern pine beetle research. Forest Farmer, 27(9): 12--13. Bennett, W.H., 1971. Silvicultural techniques will help control bark beetles. Soc. Am. Forest., South. Region Tech. Conf., pp. 289--295. Berryman, A.A., 1976. Theoretical explanation of mountain pine beetle dynamics in lodgepole pine forests. Environ. Entomol., 5(6): 1225--1233. Bozeman, P.P., 1977. Comparisons of several sources of baseline data describing site and stand conditions potentially associated with southern pine beetle infestations. M.S. Thesis, Stephen F. Austin State Univ., Nacogdoches, Texas, 65 pp. Bradford, B. and Skelly, J.M., 1976. Levels of F o m i t o p s i s annosa: influence on growth. Proc. Southwide Forest Disease Workshop. Atlanta, Ga. USDA-Forest Service, Southeastern Area S and IPF, p.1.

282 Cole, W.E. and Cahill, D.B., 1976. Cutting strategies can reduce probabilities of mountain pine beetle epidemics in lodgepole pine. J. Forest., 74(5): 294--297 Coster, J.E., 1977. Towards integrated protection from the southern pine beetle. J. Forest., 75(8): 481--484. Coulson, R.N., Hain, F.P. and Payne, T.L., 1974. Radial growth characteristics and stand density of loblolly pine in relation to the occurrence of the southern pine beetle. Environ. Entomol., 3(3): 425--428. Earles, J.M., 1976. Forest statistics for East Texas pineywoods counties. U.S.D.A., Forest Serv. Forest Res. Bull., SO-60, 40 pp. Fellin, D.G., 1976. Forest practices, silvicultural prescriptions, and the western spruce budworm. Proc. Symp. on the Spruce Budworm, Alexandria, Va. USDA Misc. Publ. No. 1327, pp. 117--121. Hamilton, D.A., Jr. and Edwards, B.M., 1976. Modeling the probability of individual tree mortality. USDA-Forest Serv. Res. Paper INT-185, 22 pp. Hedden, R.L., 1978. The need for intensive forest management to reduce southern pine beetle activity in East Texas. Southern J. Appl. Forest., 2: 19--22. Hicks, R.R., Jr., Howard, J.E., Coster, J.E. and Watterston, K.G., 1978a. The role of tree vigor in susceptibility of loblolly pine to southern pine beetle. Proc. 5th North Am. Forest Biol. Workshops, Gainesville, Fla., pp. 177--186. Hicks, R.R., Jr., Coster, J.E. and Watterston, K.G., 1978b. Reliability of field crew judgments concerning site factors associated with southern pine beetle infestations. Southwest. Entomol., 3(1): 52--58. Hodges, J.D. and Pickard, L.S., 1971. Lightning in the ecology of the southern pine beetle, Dendroctonus frontalis (Coleoptera: Scolytidae). Can. Entomol., 103(1): 44--51. Kalkstein, L.S., 1976. Effects of climatic stress on outbreaks of the southern pine beetle. Environ. Entomol., 5: 653--658. King, E.W., 1972. Rainfall and epidemics of the southern pine beetle. Environ. Entomol., 1: 279--285. Kroll, J.C. and Reeves, H.C., 1978. A simple predictive model for potential southern pine beetle activity in East Texas. Southern J. Appl. Forest., 2: 62--64. Ku, T.T., Sweeney, J.M. and Shelburne, V.B., 1976. Preliminary evaluation of site and stand characteristics associated with southern pine beetle infestations in Arkansas. Arkansas Farm Res., 25(5): 12--13. Leuschner, W.A., Burkhart, H.E., Spittle, G.D., Ragenovich, I.R. and Coulson, R.N., 1976. A descriptive study of host site variables associated with occurrence of Dendroctonus frontalis Zimm. in East Texas. Southwest. Entomol., 1 (3): 141--149. Leuschner, W.A., Matney, T.G. and Burkhart, H.E., 1977. Simulating southern pine beetle activity for pest management decisions. Can. J. Forest Res., 7(1): 138--144. Lorio, P.L., Jr., 1968. Soil and stand conditions related to southern pine beetle activity in Hardin County, Texas. J. Econ. Entomol., 61(2): 565--566. Lorio, P.L., Jr., 1978. Developing stand risk classes for the southern pine beetle. USDAForest Serv. Res. Paper $0-144, 9 pp. Lorio, P.L., Jr., and Bennett, W.H., 1974. Recurring southern pine beetle infestations near Oakdale, La. USDA-Forest Serv. Res. Paper SO-95, 6 pp. Miller, J.M. and Keen, F.P., 1960. Biology and control of the western pine beetle. USDAForest Serv. Misc. Publ., 800, 381 pp. Nie, N.H., Hull, H.C., Jenkins, J.G., Steinbrenner, K. and Bent, D.H., 1975. SPSS: Statistical Package for the Social Sciences, Second Edition. McGraw-Hill, New York, N.Y., 675 pp. Sader, S.A. and Miller, W.F., 1975. Development of a risk rating system for southern pine beetle infestation in Copiah County, Mississippi. Unpubl. MS Thesis (Sader) Miss. State Univ. Safranyik, L., Shrimpton, D.M. and Whitney, H.S., 1974. Management of lodgepole pine to reduce losses from the mountain pine beetle. Can. Dept. Environ. Forest Serv., Forest Serv. Tech. Rep. 1, Victoria, B.C., 24 pp.

283 Sartwell, C., 1971. Thinning ponderosa pine to prevent outbreaks of mountain pine beetle. Proc. Precommerical Thinning of Coastal and Intermountain Forests in the Pacific Northwest. Coop. Extension Serv. Washington State Univ., pp. 41--52. Sartwell, C. and Dolph, Jr., R.E., 1976. Silvicultural and direct control of mountain pine beetle in second-growth ponderosa pine. USDA-Forest Serv. Res. Note PNW-268, 8 pp. Sartwell, C. and Stevens, R.E., 1975. Mountain pine beetle in ponderosa pine, prospects for silvicultural control in second growth stands. J. Forest., 73(3): 136--140. Schenk, J.A., Moore, J.A., Adams, D.L. and Mahoney, R.L., 1977. A preliminary hazard rating of grand fir stands for mortality by the fir engraver. Forest Sci., 23(1): 103--110. Schmid, J.M. and Frye, R.H., 1976. Stand rating for spruce beetles. USDA-Forest Serv. Res. Note RM-309, 4 p p . Stage, A.L. and Long, E~G., 1977. The basis for integrated pest management. Proc. Group 6, XVIth IUFRO World Congress, Oslo, .Norway, pp. 19--28. Texas Forest Service, 1976. Texas forest pest activity 1974--1975 and forest pest control section biennial report. TFS Circ., 226: 1--19. Texas Forest Service, 1978. Texas forest pest activity 1976--1977 and forest pest control section biennial report. TFS Publ., 11 : 1--18. U.S.D.A., 1972. Soils Resource Guide: Southern Region. U.S. Department of Agriculture, Forest Service, 48 pp. Weitzman, 8., 1975. How to control the southern pine beetle. Southern Lumberman, Jan. 1, 1975, pp. 10--11.